CN104571533A - Device and method based on brain-computer interface technology - Google Patents
Device and method based on brain-computer interface technology Download PDFInfo
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Abstract
The invention discloses a device based on a brain-computer interface technology and a method based on the brain-computer interface technology. The device comprises a brain-computer acquisition unit, an analogue to digital converter, a signal processing and controlling unit and a voice broadcasting unit, wherein the brain-computer acquisition unit is configured to acquire brain-computer signals of subjects; the analogue to digital converter is configured to perform analogue-digital conversion to the brain-computer signals; the signal processing and controlling unit is configured to generate commands according to converted brain-computer signals and emotion parameter ranges of the subjects; the voice broadcasting unit is configured to broadcast the voice according to the commands. According to the device and the method, the brain-computer interface technology is effectively utilized, a brand new visualized communication mode is provided to the subjects, and the living quality of the subjects is helpful to enhance. In particular, the subjects who are disorder in language and normal in brain function can be effectively helped, and thus the method and the device have the positive social significance.
Description
Technical field
The present invention relates to the Applied research fields of brain-computer interface, particularly, relate to a kind of apparatus and method based on brain-computer interface technology.
Background technology
Brain-computer interface (Brain-Computer Interface, BCI) being between human brain and computing machine or other electronic equipments, set up the direct information that one do not rely on conventional brain output channel (peripheral nerve and musculature) exchange and control channel, is a kind of brand-new man-machine interactive system.Brain-computer interface, as a kind of brand-new message-switching technique, can provide a kind of new way of carrying out exchanging with the external world for people.
For the patient having aphasis severe paralysis especially simultaneously, be difficult to the emotion outwardly being expressed oneself by language or body language.In China, language deformity occupies first of the five large deformity such as with visual disabilities, physical disabilities, intelligent disability, and be 2,057 ten thousand people, account for 1.67% of population, wherein less than 7 years old children are about 800,000 people.This colony and the extraneous approach that exchanges still are confined to the approach such as sign language, hand-written, typewriting, cannot facilitate, link up with other people swimmingly.Therefore, there are the people of aphasis and extraneous apparatus and method of carrying out communication exchange easily in the urgent need to a kind of help.
Summary of the invention
According to an aspect of the present invention, a kind of device based on brain-computer interface technology is provided, comprises:
Brain wave acquisition unit, is configured to the EEG signals gathering subject;
AD conversion unit, is configured to described EEG signals to carry out analog to digital conversion;
Signal transacting and control module, be configured to generate instruction according to through the EEG signals of conversion and the emotion parameter scope of described subject; And
Voice playing unit, is configured to play voice according to described instruction.
According to the present invention's specific embodiment, described device also comprises the pre-process circuit be connected between described brain wave acquisition unit and AD conversion unit, is configured to EEG signals described in pre-service.
According to the present invention's specific embodiment, described pre-process circuit comprises: filtering circuit, is configured to carry out filtering to described EEG signals; And amplifying circuit, be configured to amplify the EEG signals through filtering.
According to the present invention's specific embodiment, described filtering circuit comprises high-pass filtering circuit, trap circuit and low-pass filter circuit; Described amplifying circuit comprises pre-amplification circuit and rearmounted amplifying circuit.
According to the present invention's specific embodiment, described pre-process circuit comprises: level adjusting circuit, is configured to the voltage of the EEG signals adjusted through amplifying.
According to the present invention's specific embodiment, described signal transacting and control module are further configured to:
Utilize different Embedded dimensions, delay spread carried out to the described EEG signals through conversion, with obtain corresponding to each Embedded dimensions, the phase space that is made up of multiple state vector;
Phase space for corresponding to each Embedded dimensions:
Calculate the distance between any two state vectors,
All distance values are divided into multiple sections of equal length difference, and add up the number of the distance value comprised in each section,
The number of distance value comprised according to each section and the probability of each section of number statistical of all distance values, and utilize described probability calculation to correspond to the information entropy of this Embedded dimensions; And
Build the unary linear regression equation of all information entropys, and generate described instruction according to the slope of described unary linear regression equation and the emotion parameter scope of described subject.
According to a further aspect of the invention, additionally provide a kind of method based on brain-computer interface technology, comprising:
Gather the EEG signals of subject;
Described EEG signals is carried out analog to digital conversion;
Instruction is generated according to through the EEG signals of conversion and the emotion parameter scope of described subject;
Voice are play according to described instruction.
According to the present invention's specific embodiment, described method also comprises: after the described EEG signals of collection, EEG signals described in pre-service.
According to the present invention's specific embodiment, described pre-service comprises filtering, amplification and the adjustment voltage through the EEG signals of amplification.
According to the present invention's specific embodiment, the step of described generation instruction comprises further:
Utilize different Embedded dimensions, delay spread carried out to the described EEG signals through conversion, with obtain corresponding to each Embedded dimensions, the phase space that is made up of multiple state vector;
Phase space for corresponding to each Embedded dimensions:
Calculate the distance between any two state vectors,
All distance values are divided into multiple sections of equal length difference, and add up the number of the distance value comprised in each section,
The number of distance value comprised according to each section and the probability of each section of number statistical of all distance values, and utilize described probability calculation to correspond to the information entropy of this Embedded dimensions; And
Build the unary linear regression equation of all information entropys, and generate described instruction according to the slope of described unary linear regression equation and the emotion parameter scope of described subject.
The present invention effectively utilizes brain-computer interface technology, for subject provide one completely newly, exchange way intuitively, contribute to the quality of life improving subject.Especially effectively helped to there is aphasis but the normally functioning subject of brains, there is positive social effect.
In summary of the invention, introduce the concept of a series of reduced form, this will further describe in embodiment part.Content part of the present invention does not also mean that the key feature and essential features that will attempt to limit technical scheme required for protection, does not more mean that the protection domain attempting to determine technical scheme required for protection.
Below in conjunction with accompanying drawing, describe advantages and features of the invention in detail.
Accompanying drawing explanation
Following accompanying drawing of the present invention in this as a part of the present invention for understanding the present invention.Shown in the drawings of embodiments of the present invention and description thereof, be used for explaining principle of the present invention.In the accompanying drawings,
Fig. 1 is the block diagram of the device based on brain-computer interface technology according to the present invention's specific embodiment;
Fig. 2 a, Fig. 2 b and Fig. 2 c respectively illustrate the schematic diagram of the information entropy of subject's different emotions according to an embodiment of the invention;
Fig. 3 is the schematic diagram of the pre-process circuit according to the present invention's specific embodiment; And
Fig. 4 is the emotion parameter scope deterministic process schematic diagram according to the present invention's specific embodiment.
Embodiment
In the following description, a large amount of details is provided the present invention can be understood up hill and dale.But those skilled in the art can understand, following description only relates to preferred embodiment of the present invention, and the present invention can be implemented without the need to one or more such details.In addition, in order to avoid obscuring with the present invention, technical characteristics more well known in the art are not described.
People is emotion animal, has various mood, such as excited, gentle and angry etc.Brain electricity (Electroencephalography, EEG) is the macroscopic appearance that the electric discharge of brain cell colony produces.When people's anxious state of mind time, brain can produce specific EEG signals.Such as, for the EEG signals collected from frontal lobe, when people's mood is different, obvious difference can be presented.The present invention utilizes the above-mentioned natural law, provides a kind of device based on brain-computer interface technology.By the EEG signals of brain-computer interface Real-time Collection subject, analyze its emotional state and translate into voice, thus facilitating subject to express the emotion of oneself.
Fig. 1 is the schematic block diagram of the device based on brain-computer interface technology according to the present invention's specific embodiment.As shown in Figure 1, this device comprises brain wave acquisition unit, AD conversion unit, signal transacting and control module and voice playing unit.These parts can connect in turn.One of ordinary skill in the art will appreciate that, this connection can be that physical connection or logic connect, and can be wired connection or wireless connections.
Brain wave acquisition cell location is the EEG signals gathering subject.
Up to the present the brain wave of human brain is divided into four kinds: δ ripple, θ ripple, α ripple and β ripple.δ ripple: deep sleep E.E.G state (scope 0.5-3HZ).When the brain frequency of people is in δ ripple, be deep sleep, automatism.Sleep quality quality and the δ ripple of people have very directly relation.The sleep of δ ripple is a kind of very deep sleep state, if oneself calling out the wavy state of approximate δ when tossing about in bed, just can break away from insomnia soon and entering deep sleep.θ ripple: the degree of depth is loosened, stress-free subconsciousness state (scope 4-8HZ).When the brain frequency of people is in θ ripple, the consciousness of people is interrupted, and health is deep to be loosened, the information for the external world present height by hint state, namely by hypnosis.θ ripple is very big for helps such as the deep memory of triggering, strengthening long-term memories, so θ ripple is called as " leading to memory and the gate learnt ".α ripple: study and the best E.E.G state (scope 8-13HZ) thought deeply.When the brain frequency of people is in α ripple, the Consciousness of people, but health loosens, and it provides consciousness and subconscious " bridge ".In this state, body and mind energy charge is minimum, and the energy that relative brain obtains is higher, and running will be quicker, smooth and easy, sharp.α ripple is considered to people's study and the best E.E.G state thought deeply.β ripple: the E.E.G state (scope 14HZ-30HZ) when anxiety, pressure, brainfag.When people regain consciousness, most of the time brain frequency is in the wavy state of β.Along with the increase of β ripple, health in tense situation, thus reduces vivo immuning system ability gradually, and now the energy ezpenditure aggravation of people, easily tired, if insufficient rest, easily piles up pressure (this is the common fault of modern).Suitable β ripple promotes notice and the development of cognitive behavior has positive role.Preferably, brain wave acquisition cell location is by the β ripple Real-time Collection in the EEG signals of subject out.Frequency due to EEG signals is the highest at about 100HZ, according to sampling thheorem, sample frequency can be chosen to be 500HZ-1000HZ in experiment.
Brain wave acquisition unit can comprise various electrode.When device uses, electrode is placed in the correct position of subject's head, such as, with scalp close contact, with the EEG signals of accurate acquisition subject.Preferably, brain wave acquisition unit comprises the dry electrode of non-intrusion type.Electrode can be two.When gathering EEG signals, an electrode can be placed in forehead, another is fixed on ear-lobe by clip.The dry electrode of non-intrusion type adopts micropin technology, ultra-high input impedance amplifier and photoelectric sense technology.Relative to traditional brain wave acquisition electrode, the dry electrode of non-intrusion type has broken away from the dependence to conducting medium, easy to use, portable, highly sensitive, be not subject to environmental constraints, thus ensure gather EEG signals accuracy.
AD conversion unit is configured to gathered EEG signals to carry out analog to digital conversion.Initial EEG signals is simulating signal.For the ease of signal transacting and control, gathered EEG signals is carried out analog to digital conversion.AD conversion unit can use existing chip, such as: model is the chip of AD7705.The conversion accuracy of this chip can reach 1/65536, advantageously ensures the accuracy of signal conversion.
Signal transacting and control module are configured to generate instruction according to the emotion parameter scope of the EEG signals through changing and described subject.
The emotion parameter scope of subject can be divided into such as 3 different range, is the emotion parameter scope of positive affect, negative affect and neutral emotion respectively.Positive affect is the emotion that people are in a cheerful frame of mind.Negative affect is the depressed emotions of people.Neutral emotion is the emotion that people's mood is gentle.Because EEG signals exists individual differences, so for every subject, the threshold value of emotion parameter scope may be slightly different.So preferably, the emotion parameter scope of subject can through carrying out learning training to this subject and obtaining.Which emotion parameter scope is EEG signals through conversion fall into as calculated, so just generates corresponding instruction, such as: positive affect instruction, negative affect instruction and neutral emotion instruction.
The acquisition of the emotion parameter scope of subject can utilize Chinese emotion picture system (ChineseAffective Picture System, CAPS).This system is the picture system that current China has majesty.It is found that different figure sector-meetings brings different psychological feelingses.Such as, the picture of lovely rabbit may allow people be in a cheerful frame of mind.Common monochromatic picture may can not cause the larger anxious state of mind of people.The picture of fearful toad may allow the popular feeling begin to detest evil.This system comprises a large amount of picture (such as flower) with positive affect, the picture (such as human skeleton) with negative affect and the picture (such as texture) with neutral emotion.Picture in this system has good emotion and arouses effect and consistance.Thus, utilize this system to obtain emotion parameter scope, ensure that objectivity and the test of emotion parameter scope.
Signal transacting and control module can adopt dsp chip and peripheral circuit thereof to realize, such as, and DSP5402 system.Dsp chip has powerful data-handling capacity, and processing speed is fast, low in energy consumption.Preferably, signal transacting and control module can also adopt the dedicated logic circuit possessing specific logical function to realize.Dedicated logic circuit is that the particular logic circuit of function solidification is formed by connecting.
Voice are play in the instruction that voice playing unit is configured to send according to signal transacting and control module.Still above example is example: for positive affect instruction, can play the voice representing that subject is joyful; For negative affect instruction, the voice representing that subject is low can be play, by that analogy.Voice playing unit can according to this instruction calls emotional speech storehouse, thus realize subject's emotion to translate into voice, gives expression to the affective state that subject is current intuitively.
Voice playing unit can comprise: the elements such as oscillator, voice unit, prime amplifier, automatic gain control circuit, anti-interference filter, output amplifier.One of ordinary skill in the art will appreciate that the specific implementation of voice playing unit, do not repeat them here.
Said apparatus effectively utilizes brain-computer interface technology, overcomes in prior art and fails to realize the mood of subject being translated into voice thus expressing the deficiency of the emotion of subject intuitively; For subject provide one completely newly, exchange way intuitively, contribute to the quality of life improving subject.Especially effectively helped to there is aphasis but the normally functioning subject of brains, there is positive social effect.
According to the present invention's specific embodiment, above-mentioned signal transacting and control module can be further configured to and EEG signals is carried out phase space reconfiguration, utilize information entropy and unitary linear model to generate instruction afterwards.Below this is described in detail.One of ordinary skill in the art will appreciate that, following content all can adopt the dedicated logic circuit possessing specific logical function to realize.
First, utilize different Embedded dimensions, carry out delay spread to through analog-to-digital EEG signals, with obtain corresponding to each Embedded dimensions, the phase space that is made up of multiple state vector.
General time series signal is mainly studied in time domain or transform domain.EEG signals belongs to chaotic time series signals.Preferably, for EEG signals, no matter be the calculating of chaos invariant, the foundation of chaotic model and prediction can carry out in phase space.Phase space reconfiguration is a very important step of process chaotic time series signals.Phase space be one in order to represent the space of an all possible state of system, namely each possible state of system has the point of a corresponding phase space.Takens theorem ensure that and can reconstruct a phase space of equal value under topological significance with motive power system from one dimension chaos time sequence, and the time series of namely surveying in the presence of noise can be embedded in phase space.That is, suppose the time series recording certain system, the object of phase space reconfiguration is exactly that the time series of this actual measurement is embedded in a phase space.Time series of this actual measurement be phase space a certain combination of a little change of (state of system).Equation due to motive power system is unknown, so its attractor (topological parameter of power system) can not be obtained from the equation of system, but can in selected embedded space, from the time series data of actual measurement, reconstruct the attractor of original system and make it keep the unchangeability of former all internal characteristicses.Phase space reconfiguration is exactly to make the attractor after reconstruct and real attractor accomplish homeomorphic as far as possible.
In this embodiment, coordinate time expander method is adopted to carry out phase space reconfiguration.If the L point One-dimension Time Series of EEG signals sampling gained is expressed as [x (1), x (2) ..., x (L)], Embedded dimensions is m, and time delay is S.Wherein, the natural number that m can get 4 to 30 respectively calculates respectively, and S can get 12.Such as, for each Embedded dimensions m, m=5, EEG signals, after delay spread, obtains the phase space be made up of n state vector.This n state vector represents n coordinate points (system state) in phase space, forms the movement locus of a power system.
Wherein, a kth state vector is:
X
k＝[x(k),x(k+S),x(k+2S),…,x(k+(m-1)S)]
T,
k＝1,2,…,n，n＝L-(m-1)S (1)
Then, phase space for corresponding to each Embedded dimensions: calculate the distance between any two state vectors, all distance values are divided into multiple sections of equal length difference, and add up the number of the distance value comprised in each section, the number of distance value comprised according to each section and the probability of each section of number statistical of all distance values, and utilize this probability calculation to correspond to the information entropy of this Embedded dimensions.Calculating for the phase space corresponding to each Embedded dimensions will be described in detail below.
1) free position vector X is calculated
kand the distance between all the other state vectors.This distance can be standard European distance therebetween.Such as, two m tie up state vector a (x
11, x
12..., x
1m) and b (x
21, x
22..., x
2m) between Euclidean distance according to as shown in the formula (2) calculate, wherein S
krepresent the standard deviation of a kth component.
One of ordinary skill in the art will appreciate that, except standard Euclidean distance, the present invention can also adopt other distances.Employing standard Euclidean distance can overcome Euclidean distance by the equivalent shortcoming of important attribute, to become expectation be 0 by unified for different components, and variance is the standard profile of 1.For classification unified standard afterwards.
2) all distance values are divided into multiple sections of equal length difference, and add up the number of the distance value comprised in each section.
Suppose total Ns section, Ns can get the natural number between 10 to 20.According to the present invention's specific embodiment, suppose that ultimate range is Dmax, minor increment is Dmin.Calculate the difference between ultimate range and minor increment, D=Dmax-Dmin.If d=D/Ns is the length difference of adjacent sections.Then the length of section is respectively Dmin+d, Dmin+2d ..., Dmax.All distance values are divided into by size this Ns section.Suppose Dmin+d<Di<Dmin+2d, so distance Di is divided into the section that length is Dmin+2d.Then add up in this Ns section, the number of the distance that each section comprises.
3) number of distance value comprised according to each section and the probability of each section of number statistical of all distance values, and utilize above-mentioned probability calculation to correspond to the information entropy of this Embedded dimensions.Information entropy can be understood as the order degree of certain system information, and the order of information entropy more Iarge-scale system is lower.Can by the ratio of the statistical number with sum that calculate each section draw this section a probability distribution Pi (i=1,2 ..., NS).Probability P i finally can be utilized to calculate the information entropy corresponding to this Embedded dimensions m by formula (3).
E(m)＝∑Pi*log
2(Pi) (3)
Change m value, repeat above calculation procedure, draw the information entropy E (m) of corresponding each Embedded dimensions m.Fig. 2 a, Fig. 2 b and Fig. 2 c respectively illustrate the schematic diagram of the information entropy of subject's different emotions according to an embodiment of the invention.Wherein, horizontal ordinate is Embedded dimensions m, and ordinate is information entropy, and unit is bit.In Fig. 2 a, along with the increase of Embedded dimensions, information entropy becomes large gradually.In Fig. 2 b, along with the increase of Embedded dimensions, information entropy significantly reduces.In Fig. 2 c, along with the increase of Embedded dimensions, information entropy slowly reduces.
In a word, above-mentioned calculating reconstructs this system all possible state during this period of time from the One-dimension Time Series surveyed, and then calculates the probability of conversion mutually between these states.State point deficiency that is overstocked or that excessively dredge when above-mentioned computation process can overcome phase space reconfiguration.Be only the preferred embodiment provided above persons of ordinary skill in the art may appreciate that, it is not construed as limiting the invention.
Finally, build the unary linear regression equation of all information entropys corresponding to each Embedded dimensions, and generate described instruction according to the slope of this unary linear regression equation and the emotion parameter scope of this subject.
Still with Fig. 2 a, information entropy schematic diagram shown in Fig. 2 b and Fig. 2 c is example, the slope of the unary linear regression equation of information entropy is wherein respectively 0.4,-0.005 and-0.025, suppose the positivity of this subject, negativity and neutral emotion parameter scope are [0.1 respectively, 1], [-0.01,-0.001], [-0.1,-0.01], then can judge: it is positivity that the information entropy shown in Fig. 2 a have expressed the current emotion of subject, it is negativity that information entropy shown in Fig. 2 b have expressed the current emotion of subject, information entropy shown in Fig. 2 c have expressed the current emotion of subject for neutral.Thus, signal transacting and control module can send corresponding instruction respectively to voice playing unit.
According to the present invention's specific embodiment, said apparatus can also comprise the pre-process circuit be connected between described brain wave acquisition unit and AD conversion unit, is configured to pre-service EEG signals.The amplitude of EEG signals probably about 2 to 100 microvolts, and along with the noise effect of complexity.Pre-process circuit effectively can increase the intensity of useful signal, avoids it to be subject to noise, and the accuracy of speech play is improved, thus improves subject's experience.
Alternatively, this pre-process circuit can comprise filtering circuit and amplifying circuit.Filtering circuit is configured to carry out filtering to EEG signals; Amplifying circuit is configured to amplify the EEG signals through filtering.More convenient in order to calculate, EEG signals can be amplified 15000 to 20000 times.Filtering circuit and amplifying circuit coordinate, and effectively can improve the signal to noise ratio (S/N ratio) of EEG signals.
Alternatively, filtering circuit can comprise high-pass filtering circuit, trap circuit and low-pass filter circuit; Amplifying circuit can comprise pre-amplification circuit and rearmounted amplifying circuit.Alternatively, pre-process circuit can also comprise level adjusting circuit, and it is configured to the voltage of the EEG signals adjusted through amplifying.
Fig. 3 shows the schematic diagram of the pre-process circuit according to the present invention's specific embodiment.As shown in Figure 3, Vin+ is input EEG signals (such as, frontal lobe signal), and Vin-is reference signal (such as, the signal from ear-lobe), and Vout is the voltage signal that pre-process circuit exports.In figure 3, part A is first order signal amplification circuit.Part B and D part are high-pass filtering circuits.C part is trap circuit.E part is low-pass filter circuit.F part is intergrade amplifying circuit.G part is second order active low-pass filter circuit.H part is the reference level Circuit tuning of EEG signals.As shown in Figure 3, A, B, C, D, E, F, G and H part connection in sequential series.
Wherein, part A shown in Fig. 3 is first order signal amplification circuit.Preferably, first order signal amplification circuit is measuring amplifier.It can suppress the interference comprising power frequency, electrostatic and electromagnetic coupled etc. preferably; And it does not have the offset voltage of millivolt level and the temperature drift of microvolt level.Such as, can adopt chip I NA128, it has the feature of high cmrr, high precision, low-power consumption, low maladjustment voltage, low drifting and high stable gain, can realize any gain selection from 1 to 10000.Its enlargement factor is determined by formula Au=1+50k Ω/R3.When the such integrated amplifier of measuring amplifier is as prime amplifier, due to the existence of polarizing voltage, the gain of amplifier can only within 100 times.According to the preferred embodiment of the present invention, this circuit adopts as figure connection, and R3=2.5k Ω, enlargement factor is 21 times.
Part B and D part are high-pass filtering circuits.Part B can be second order active Hi-pass filter, in order to guarantee the filter effect of high pass, adds one-level single order passive high three-way filter.Passive filter is simpler than active filter circuit, and cost is low, so under the prerequisite of calculation requirement that can meet EEG signals, passive filter can be selected to ensure filter effect.The corner frequency of Hi-pass filter can be 14HZ.
C part is trap circuit.Increase trap circuit, effectively avoid the Hz noise of 50HZ.
E part is low-pass filter circuit.Can adopt second order active low-pass filter circuit to realize, wherein corner frequency can be 30HZ.Active filter circuit is that volume is little, lightweight and performance is good compared with the benefit of passive filter circuit.
F part is intergrade amplifying circuit.Intergrade amplifying circuit is the main amplifying circuit of whole Acquisition Circuit, and such as adopt OP482GP amplifier to realize, enlargement factor can be 11 times.In order to avoid the noise introduced is excessive, enlargement factor is here also unsuitable excessive, and the requirement of overall enlargement factor can be realized by compensating circuit below.
G part is second order active low-pass filter circuit.G part effectively prevent prime and amplifies the noise introduced in the process of EEG signals.H part is the reference level Circuit tuning of EEG signals.G part and H part can form rear class signal compensation circuit jointly.Signal compensation circuit can adjust the EEG signals after prime amplification, meets the input requirements of AD conversion unit better.Suppose to adopt AD7705 chip as AD conversion unit, the input voltage that it requires is 0 ~ 2.5V, so need signal compensation circuit (G & H) that EEG signals is adjusted to this scope., as figure, first, U7, resistance R18 and R20 form reverse amplification circuit, and enlargement factor is 101 times.This circuit is first amplified to volt level EEG signals, further for signal lifting is afterwards prepared.So, the enlargement factor of whole pre-process circuit is Au=21*11*101, is about 20000 times.U7 forms totalizer with resistance R18, R19, R20 and R21 again, and signal is lifted to 0 ~ 2.5V.
In above-mentioned pre-process circuit, by filtering circuit, amplifying circuit, level adjusting circuit and the connection between it, effectively ensure that the signal to noise ratio (S/N ratio) of EEG signals, and then ensure that subsequent treatment and the calculating of EEG signals.
According to a further aspect in the invention, a kind of method based on brain-computer interface technology is additionally provided.The method comprises the following steps:
Gather the EEG signals of subject;
Described EEG signals is carried out analog to digital conversion;
Instruction is generated according to through the EEG signals of conversion and the emotion parameter scope of described subject;
Voice are play according to described instruction.
According to the present invention's specific embodiment, described method also comprises: after the described EEG signals of collection, EEG signals described in pre-service.
According to the present invention's specific embodiment, described pre-service comprises filtering, amplification and the adjustment voltage through the EEG signals of amplification.
According to a specific embodiment of the present invention, described pre-service comprises: a) first order signal amplifies; B) high-pass filtering; C) trap; D) high-pass filtering again; E) low-pass filtering; F) intermediate signal amplifies; F) low-pass filtering again; H) the reference level adjustment of EEG signals.
According to the present invention's specific embodiment, the step of described generation instruction comprises further:
Utilize different Embedded dimensions, delay spread carried out to the described EEG signals through conversion, with obtain corresponding to each Embedded dimensions, the phase space that is made up of multiple state vector;
Phase space for corresponding to each Embedded dimensions:
Calculate the distance between any two state vectors,
All distance values are divided into multiple sections of equal length difference, and add up the number of the distance value comprised in each section,
The number of distance value comprised according to each section and the probability of each section of number statistical of all distance values, and utilize described probability calculation to correspond to the information entropy of this Embedded dimensions; And
Build the unary linear regression equation of all information entropys, and generate described instruction according to the slope of described unary linear regression equation and the emotion parameter scope of described subject.
Those of ordinary skill in the art can understand the step of the method, realization and advantage about the description of the device based on brain-computer interface technology above by reading, and therefore repeat no more here.
Utilize said method, carry out fundamental characteristics experiment, with the validity of method of proof for 30 subjects.Subject is young student, 15 boy students, 15 schoolgirls, 24 years old mean age, healthy and righthanded, and normal visual acuity (containing correcting defects of vision), all without psychological history of disease.Subject on pretreatment one day sleep quality is good, participates in this experiment voluntarily.On pretreatment, leading subject to be familiar with experimental situation, and easily talk with subject, to eliminate its nervous psychology, making it to be in relaxation state when entering experiment.As previously mentioned, when presenting the picture of different emotions to subject, the EEG signals gathered also has notable difference.So in an experiment, choosing 30 different emotions from Chinese emotion picture system stimulates picture, wherein, the picture with positive affect, the picture with negative affect and with each 10 of the picture of neutral emotion.
First, test according to process flow diagram shown in Fig. 4, to determine the emotion parameter scope of different emotions.Experimental arrangement can be write based on Eprime platform.In experimentation, shuffle 30 pictures, plays every pictures 2 seconds, has the time of 5 seconds between adjacent picture, for selecting the emotion expressed for subject.
Such as, subject does button reaction after seeing the picture often opening broadcasting.Subject sees allows it feel the F key of picture then on keypad of positive affect, see allow it feel J key then pressed by the picture of negative affect, see allow it not have space (SPACE) key then pressed by picture that too large emotion fluctuates.Experimental arrangement is utilized automatically to record the behavior of subject.This experimentation approximately can continue half an hour.Thus, the emotion parameter scope of the positive affect of each subject, negative affect and neutral emotion can be obtained.
Then, again according to similar flow process as shown in Figure 4, to subject's shuffle picture, wherein, playing in the 5 second time between adjacent picture, any key on keyboard is pressed without the need to subject.In this step, based on obtained above-mentioned parameter scope, in gathered subject's EEG signals after signal processing unit processes, automatically play related voice.
Compare known by play voice with the pictorial information in Chinese emotion picture system, the accuracy that said apparatus provided by the present invention and method show emotion is up to more than 75%.
The present invention is illustrated by above-described embodiment, but should be understood that, above-described embodiment just for the object of illustrating and illustrate, and is not intended to the present invention to be limited in described scope of embodiments.In addition it will be appreciated by persons skilled in the art that the present invention is not limited to above-described embodiment, more kinds of variants and modifications can also be made according to instruction of the present invention, within these variants and modifications all drop on the present invention's scope required for protection.Protection scope of the present invention defined by the appended claims and equivalent scope thereof.
Claims (10)
1., based on a device for brain-computer interface technology, comprising:
Brain wave acquisition unit, is configured to the EEG signals gathering subject;
AD conversion unit, is configured to described EEG signals to carry out analog to digital conversion;
Signal transacting and control module, be configured to generate instruction according to through the EEG signals of conversion and the emotion parameter scope of described subject; And
Voice playing unit, is configured to play voice according to described instruction.
2. device as claimed in claim 1, it is characterized in that, described device also comprises the pre-process circuit be connected between described brain wave acquisition unit and AD conversion unit, is configured to EEG signals described in pre-service.
3. device as claimed in claim 2, it is characterized in that, described pre-process circuit comprises:
Filtering circuit, is configured to carry out filtering to described EEG signals; And
Amplifying circuit, is configured to amplify the EEG signals through filtering.
4. device as claimed in claim 3, is characterized in that,
Described filtering circuit comprises high-pass filtering circuit, trap circuit and low-pass filter circuit;
Described amplifying circuit comprises pre-amplification circuit and rearmounted amplifying circuit.
5. device as claimed in claim 4, it is characterized in that, described pre-process circuit comprises:
Level adjusting circuit, is configured to the voltage of the EEG signals adjusted through amplifying.
6. device as claimed in claim 1, it is characterized in that, described signal transacting and control module are further configured to:
Utilize different Embedded dimensions, delay spread carried out to the described EEG signals through conversion, with obtain corresponding to each Embedded dimensions, the phase space that is made up of multiple state vector;
Phase space for corresponding to each Embedded dimensions:
Calculate the distance between any two state vectors,
All distance values are divided into multiple sections of equal length difference, and add up the number of the distance value comprised in each section,
The number of distance value comprised according to each section and the probability of each section of number statistical of all distance values, and utilize described probability calculation to correspond to the information entropy of this Embedded dimensions; And
Build the unary linear regression equation of all information entropys, and generate described instruction according to the slope of described unary linear regression equation and the emotion parameter scope of described subject.
7., based on a method for brain-computer interface technology, comprising:
Gather the EEG signals of subject;
Described EEG signals is carried out analog to digital conversion;
Instruction is generated according to through the EEG signals of conversion and the emotion parameter scope of described subject;
Voice are play according to described instruction.
8. method as claimed in claim 7, it is characterized in that, described method also comprises:
After the described EEG signals of collection, EEG signals described in pre-service.
9. method as claimed in claim 8, is characterized in that, described pre-service comprises filtering, amplification and the adjustment voltage through the EEG signals of amplification.
10. method as claimed in claim 9, it is characterized in that, the step of described generation instruction comprises further:
Utilize different Embedded dimensions, delay spread carried out to the described EEG signals through conversion, with obtain corresponding to each Embedded dimensions, the phase space that is made up of multiple state vector;
Phase space for corresponding to each Embedded dimensions:
Calculate the distance between any two state vectors,
All distance values are divided into multiple sections of equal length difference, and add up the number of the distance value comprised in each section,
The number of distance value comprised according to each section and the probability of each section of number statistical of all distance values, and utilize described probability calculation to correspond to the information entropy of this Embedded dimensions; And
Build the unary linear regression equation of all information entropys, and generate described instruction according to the slope of described unary linear regression equation and the emotion parameter scope of described subject.
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